Thus, the requirement of featural overlap
to achieve generalisation has been a basic assumption
made by both proponents and critics of connectionist
models of cognition.
However, convolutional neural nets are designed precisely
to overcome this limitation in relation to generalisation
to unseen positions, and this ability has been a key factor
in their widespread adoption in deep image recognition
models.
The mechanism by which they achieve this is simply the sharing
of the same feature detectors across all positions in the
image, which avoids the independence of weights
criticised by [1]}.
More abstractly, this weight sharing can be understood as
an implementation of the requirement for the network's
behaviour to be symmetric under spatial translations.
Thus, in addition to generalisation being possible between
inputs related by featural similarity, we can also obtain
generalisation between inputs related by a symmetry.
